An MCP connector is what lets your AI assistant pull in real financial data, and they're not all equal. Some are properly licensed, some mix up companies, some only go back a year, and some can't show where an answer came from. Here's what to look for, who offers what, and how to test one.
By
Bigdata team
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Key Takeaways
Your choice of connector is really a choice about data. It sets the limit on what your assistant can answer.
Four things matter most: data licensed for AI use, getting the company right (especially beyond big US names), how far back the data goes, and whether answers link to their sources.
You can test most connectors in an afternoon with three questions you already know the answers to.
You pay for how much data each question pulls in, not per seat, so a connector that fetches only what's needed saves money as well as giving better answers.
An MCP connector is the piece that lets your AI assistant pull in outside data. In finance, that choice matters a lot, because the connector decides what your assistant can actually see. Connectors built for finance differ in four ways: whether the data is licensed for AI use, whether they match names to the right company, how far back the data goes, and whether you can check where each answer came from. Below, we walk through each one, look at who offers what today, and share a quick way to test a connector.
How is a financial connector different from a regular one?
Most MCP servers do simple jobs: they let an assistant check the weather, open a support ticket or look up a customer record. Financial connectors have an extra job. Most of the data they serve belongs to someone else and is licensed, and that comes with rules about who can see it, how long it can be stored, and whether an AI summary counts as passing it on. So even when two connectors look the same on the surface (a "get price" tool here, a "search filings" tool there), the financial one is quietly checking permissions in the background. A to-do app never has to think about that.
What should you look for in a financial connector?
Four things separate a connector you can rely on from one that makes things up or shows data it shouldn't:
Is the data licensed for AI? Data you're allowed to read on a terminal screen isn't automatically data an AI is allowed to read, store and repeat back. Ask the vendor directly.
Does it get the company right? The same company can show up under different tickers, languages, exchanges or former names, and a good connector knows they're all the same business (this is often called entity resolution). Plenty of connectors handle the biggest US companies fine, then start slipping on smaller or non-US names.
How far back does the data go? A connector that only holds the last 12 months can't tell you whether something has happened before. That rules out most backtesting and "have we seen this before?" questions.
Can you check the source? A grounded answer, meaning one backed by real sources, should let you click through to the exact passage it used. If it can't, there's no way to tell whether the assistant used the connector or just filled the gaps from memory.
Which financial MCP connectors are available today?
They fall into three rough groups: big institutional data providers running their own servers, developer-friendly providers that added an MCP server to their existing API, and smaller servers built for one specific job. This isn't a complete list (new ones appear almost every month), but it gives you a good feel for each group.
Note: Bigdata.com partners with many of the firms listed below to provide access to their data through the Bigdata MCP.
Big institutional providers
S&P Global (Kensho) offers an AI-ready API that comes with an MCP server. It gives assistants access to S&P Capital IQ financials, deals and earnings call transcripts, and since a February 2026 update, private company financials and Capital IQ estimates too. It's also testing MCP Apps (in beta), which show the data as interactive charts and tables right inside the chat instead of plain text.
FactSet launched what it called the industry's first production-grade MCP server in December 2025. It gives assistants real-time access to FactSet's data, using the same APIs it says hundreds of institutional clients already rely on for critical work.
LSEG hosts the MCP server for you. Instead of handing you software to run yourself, it runs the server itself and gives you access to the data you already license through LSEG Workspace and Financial Analytics. It keeps track of who's allowed to see what and where the data came from, and it works with Claude, Microsoft Copilot Studio and Databricks.
Bloomberg has said it's lining up its AI plans with MCP, but at the time of writing it hasn't released an open server you can run yourself the way FactSet has. There are community-built workarounds that connect Bloomberg's blpapi library to tools like Claude Code and Cursor, but you still need an active Bloomberg Terminal subscription to use them.
Bigdata.com belongs in this group too. It's built to keep an assistant's answers tied to real financial news, filings and research, with a citation on every claim. Its MCP server has 21 read-only tools. One (find_securities) finds the right company from a name or ticker. Others pull quick profiles, or tearsheets, for public and private companies, ETFs, portfolios, whole markets and countries. And there's search across filings, earnings call transcripts, broker research, podcasts, a firm's own private documents and the open web. It's SOC 2 Type II and ISO 27001 certified, and says client data is never used to train models.
Developer-friendly providers
Alpha Vantage runs its own MCP server covering stocks, ETFs, options, currencies, crypto, commodities and company fundamentals. It also calculates more than 60 technical and economic indicators for you, all through one connection.
Financial Modeling Prep (FMP) turns its API into an MCP server. You get income statements, balance sheets, cash flow, valuation ratios, analyst estimates and SEC filings, in the same format whether the company is big or small, US or not.
EODHD puts prices, fundamentals, economic data, corporate actions and options data from more than 70 exchanges into one connection. It even keeps its own documentation inside the server, so the assistant can look up how to use it.
Tiingo and Financial Datasets (financialdatasets.ai) both keep their data format the same across asset types, which makes life easier for anyone building on top of them. Financial Datasets was built with AI agents in mind from day one: it breaks SEC filings into sections instead of sending the whole document at once.
Specialized servers
Nasdaq Data Link (formerly Quandl) is where to go for economic indicators, retail trading data and other alternative data that most price-and-fundamentals connectors don't carry.
Alpaca and Tradier don't just fetch data, they can trade. You can place, change and cancel stock and options orders through the same connection that gives you quotes and account info.
MarketXLS, according to the same roundup, is the only one here that gives you real-time options chains with the Greeks calculated live.
Beyond these, there's a long tail of lighter, open-source and marketplace servers built on free or aggregated market data. They're quick to set up and fine for prototyping, but don't expect much licensed institutional data.
Important to mention that this as a snapshot and not a ranking. The right pick depends on which of the four things above (licensing, getting the company right, history and citations) matter most for what you're doing.
How do you test a connector before committing?
Pick three questions you already know the answers to. Make one about something recent ("what did company X report last quarter?"), one that needs history ("has this metric ever been this low before?"), and one that tests company matching ("which of these five similar-sounding companies is this filing about?"). Run those before anything else. If the connector gets them wrong, or can't show you a source you can click, you've learned what you needed to know in one afternoon.
How much does an MCP connector cost?
With most connectors, what you pay depends on how much data each question pulls in, not how many people use it. That makes sense: it's an AI agent making the calls, not a person with a login. It's also a big change from how many of these providers have traditionally charged. Some of the bigger names bundle their connector with a data license you may already have, while newer providers usually charge by usage from the start. Either way, what really matters is whether you can see what a specific workflow will cost before rolling it out to your team, not the headline price. For example, Bigdata.com says that sending the model only the passages that actually answer the question, instead of whole documents, cuts the amount of text it has to process by about 90% for the same question. That's the kind of thing that makes a real difference to the bill.
What won't a connector do for you?
A connector gives your assistant access to data. It doesn't do the analysis for you, and it doesn't replace the data license or compliance sign-off your firm needs to use that data in the first place. It also won't save you from a vague question: even with great data, an assistant can misread a table or mix up two companies if you don't ask it to show its work. Think of the connector as the foundation, not the whole house.
